






"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCamelCase_							      = logging.getLogger(__name__)

def 							snake_case (  ):
			UpperCAmelCase_	:					Union[str, Any]     	=   argparse.ArgumentParser(
			    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."		)
			parser.add_argument("--file_path"      ,type=A__      ,default="data/dump.txt"      ,help="The path to the data."		)
			parser.add_argument("--tokenizer_type"      ,type=A__      ,default="bert"      ,choices=["bert", "roberta", "gpt2"]		)
			parser.add_argument("--tokenizer_name"      ,type=A__      ,default="bert-base-uncased"      ,help="The tokenizer to use."		)
			parser.add_argument("--dump_file"      ,type=A__      ,default="data/dump"      ,help="The dump file prefix."		)
			UpperCAmelCase_	:					Dict     	=   parser.parse_args()

			logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""		)
			if args.tokenizer_type == "bert":
						UpperCAmelCase_	:					Any     	=   BertTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					Tuple     	=   tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
						UpperCAmelCase_	:					str     	=   tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						UpperCAmelCase_	:					Optional[Any]     	=   RobertaTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					List[Any]     	=   tokenizer.special_tokens_map["cls_token"]  # `<s>`
						UpperCAmelCase_	:					int     	=   tokenizer.special_tokens_map["sep_token"]  # `</s>`
			elif args.tokenizer_type == "gpt2":
						UpperCAmelCase_	:					Optional[int]     	=   GPTaTokenizer.from_pretrained(args.tokenizer_name		)
						UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
						UpperCAmelCase_	:					Dict     	=   tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

			logger.info(F"""Loading text from {args.file_path}"""		)
			with open(args.file_path      ,"r"      ,encoding="utf8"		) as fp:
						UpperCAmelCase_	:					Any     	=   fp.readlines()

			logger.info("Start encoding"		)
			logger.info(F"""{len(A__		)} examples to process."""		)

			UpperCAmelCase_	:					List[Any]     	=   []
			UpperCAmelCase_	:					Any     	=   0
			UpperCAmelCase_	:					Optional[int]     	=   1_00_00
			UpperCAmelCase_	:					Dict     	=   time.time()
			for text in data:
						UpperCAmelCase_	:					Union[str, Any]     	=   F"""{bos} {text.strip()} {sep}"""
						UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode(A__      ,add_special_tokens=A__		)
						rslt.append(A__		)

						iter += 1
						if iter % interval == 0:
									UpperCAmelCase_	:					Optional[int]     	=   time.time()
									logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""		)
									UpperCAmelCase_	:					str     	=   time.time()
			logger.info("Finished binarization"		)
			logger.info(F"""{len(A__		)} examples processed."""		)

			UpperCAmelCase_	:					str     	=   F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
			UpperCAmelCase_	:					Dict     	=   tokenizer.vocab_size
			if vocab_size < (1 << 16):
						UpperCAmelCase_	:					int     	=   [np.uintaa(A__		) for d in rslt]
			else:
						UpperCAmelCase_	:					str     	=   [np.intaa(A__		) for d in rslt]
			random.shuffle(rslt_		)
			logger.info(F"""Dump to {dp_file}"""		)
			with open(A__      ,"wb"		) as handle:
						pickle.dump(rslt_      ,A__      ,protocol=pickle.HIGHEST_PROTOCOL		)


if __name__ == "__main__":
			main()











"""simple docstring"""


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class 						__UpperCamelCase     :
					lowercase_    :				int
					lowercase_    :				TreeNode | None      =  None
					lowercase_    :				TreeNode | None      =  None


__SCREAMING_SNAKE_CASE   =   namedtuple('CoinsDistribResult', 'moves excess')


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(a__ ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left ) + count_nodes(node.right ) + 1

						def count_coins(a__ ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left ) + count_coins(node.right ) + node.data

						if count_nodes(a__ ) != count_coins(a__ ):
												raise ValueError('The nodes number should be same as the number of coins' )

						# Main calculation
						def get_distrib(a__ ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0     ,      1 )

												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			get_distrib(node.left )
												lowerCAmelCase	, lowerCAmelCase   :str						=			get_distrib(node.right )

												lowerCAmelCase   :str						=			1 - left_distrib_excess
												lowerCAmelCase   :Optional[Any]						=			1 - right_distrib_excess

												lowerCAmelCase   :Any						=			(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(a__ )
												    + abs(a__ )
												)
												lowerCAmelCase   :Tuple						=			node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(a__     ,      a__ )

						return get_distrib(a__ )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()